High 10 Open-source Reasoning Fashions in 2026

Introduction 

AI in 2026 is shifting from uncooked textual content mills to brokers that act and purpose. Specialists predict a give attention to sustained reasoning and multi-step planning in AI brokers. In observe, this implies LLMs should assume earlier than they converse, breaking duties into steps and verifying logic earlier than outputting solutions. Certainly, current analyses argue that 2026 might be outlined by reasoning-first LLMs-models that deliberately use inside deliberation loops to enhance correctness. These fashions will energy autonomous brokers, self-debugging code assistants, strategic planners, and extra. 

On the similar time, real-world AI deployment now calls for rigor: “the query is now not ‘Can AI do that?’ however ‘How nicely, at what value, and for whom?’”. Thus, open fashions that ship high-quality reasoning and sensible effectivity are vital.

Reasoning-centric LLMs matter as a result of many rising applications- from superior QA and coding to AI-driven research-require multi-turn logical chains. For instance, agentic workflows depend on fashions that may plan and confirm steps over lengthy contexts. Benchmarks of 2025 present that specialised reasoning fashions now rival proprietary techniques on math, logic, and tool-using duties. Briefly, reasoning LLMs are the engines behind next-gen AI brokers and decision-makers.

On this weblog, we are going to discover the highest 10 open-source reasoning LLMs of 2026, their benchmark efficiency, architectural improvements, and deployment methods.

What Is a Reasoning LLM?

Reasoning LLMs are fashions tuned or designed to excel at multi-step, logic-driven duties (puzzles, superior math, iterative problem-solving) fairly than one-shot Q&A. They usually generate intermediate steps or ideas of their outputs.

As an example, answering “If a prepare goes 60 mph for 3 hours, how far?” requires computing distance = pace×time earlier than answering-a easy reasoning process. A real reasoning mannequin would explicitly embrace the computation step in its response. Extra advanced duties equally demand chain-of-thought. In observe, reasoning LLMs typically have pondering mode: both they output their chain-of-thought in textual content, or they run hidden iterations of inference internally.

Trendy reasoning fashions are these refined to excel at advanced duties finest solved with intermediate steps, comparable to puzzles, math proofs, and coding challenges. They usually embrace specific reasoning content material within the response. Importantly, not all LLMs should be reasoning LLMs: easier duties like translation or trivia don’t require them. Actually, utilizing a heavy reasoning mannequin in all places could be wasteful and even “overthinking.” The bottom line is matching instruments to duties. However for superior agentic and STEM functions, these reasoning-specialist LLMs are important.

Architectural Patterns of Reasoning-First Fashions

Reasoning LLMs typically make use of specialised architectures and coaching:

• Combination of Specialists (MoE): Many high-end reasoning fashions use MoE to pack trillions of parameters whereas activating solely a fraction per token. For instance, Qwen3-Subsequent-80B prompts solely 3B parameters by way of 512 consultants, and GLM-4.7 is 355B whole with ~32B lively. Moonshot’s Kimi K2 makes use of ~1T whole parameters (32B lively) throughout 384 consultants. Nemotron 3 Nano (NVIDIA) makes use of ~31.6B whole (3.2B lively, by way of a hybrid MoE Transformer). MoE permits enormous mannequin capability for advanced reasoning with decrease per-token compute.
• Prolonged Context Home windows: Reasoning duties typically span lengthy dialogues or paperwork. Thus many fashions natively help enormous context sizes (128K-1M tokens). Kimi K2 and Qwen-coder fashions help 256K (extensible to 1M) contexts. LLaMA 3.3 extends to 128K tokens. Nemotron-3 helps as much as 1M context size. Lengthy context is essential for multi-step plan monitoring, device historical past, and doc understanding.
• Chain-of-Thought and Pondering Modes: Architecturally, reasoning LLMs typically have specific “pondering” modes. For instance, Kimi K2 solely outputs in a “pondering” format with … blocks, implementing chain-of-thought. Qwen3-Subsequent-80B-Pondering routinely features a tag in its immediate to power reasoning mode. DeepSeek-V3.2 exposes an endpoint that by default produces an inside chain of thought earlier than ultimate solutions. These modes could be toggled or managed at inference time, buying and selling off latency vs. reasoning depth.
• Coaching Strategies: Past structure, many reasoning fashions bear specialised coaching. OpenAI’s gpt-oss-120B and NVIDIA’s Nemotron all use RL from suggestions (typically with math/programming rewards) to spice up problem-solving. For instance, DeepSeek-R1 and R1-Zero have been skilled with large-scale RL to straight optimize reasoning capabilities. Nemotron-3 was fine-tuned with a mixture of supervised fine-tuning (SFT) on reasoning knowledge and multi-environment RL . Qwen3-Subsequent and GPT-OSS each undertake “pondering” coaching the place the mannequin is explicitly skilled to generate reasoning steps. Such focused coaching yields markedly higher efficiency on reasoning benchmarks.
• Effectivity and Quantizations: To make these giant fashions sensible, many use aggressive quantization or distillation. Kimi K2 is natively INT4-quantized. Nemotron Nano was post-quantized to FP8 for sooner throughput. GPT-OSS-20B/120B are optimized to run on commodity GPUs. Moonshot’s MiniMax additionally emphasizes an “environment friendly design”: solely 10B activated parameters (with ~230B whole) to suit advanced agent duties.

Collectively, these patterns – MoE scaling, enormous contexts, chain-of-thought coaching, and cautious tuning – outline right now’s reasoning LLM architectures.

1. GPT-OSS-120B

GPT-OSS-120B is a production-ready open-weight mannequin launched in 2025.  It makes use of a Combination-of-Specialists (MoE) design with 117B whole / 5.1B lively parameters. 

GPT-OSS-120B achieves near-parity with OpenAI’s o4-mini on core reasoning benchmarks, whereas operating on a single 80GB GPU. It additionally outperforms different open fashions of comparable measurement on reasoning and gear use.

It additionally is available in a 20B model optimized for effectivity: the 20B mannequin matches o3-mini and may run on simply 16GB of RAM, making it superb for native or edge use. Each fashions help chain-of-thought with tags and full device integration by way of APIs. They help excessive instruction-following high quality and are absolutely Apache-2.0 licensed.

Key specs: 

Strengths and Limits

• Execs: Close to-proprietary reasoning (AIME/GPQA parity), single-GPU viable, full CoT/device APIs for brokers.
• Cons: 120B deploy nonetheless wants tensor-parallel for <80GB setups; group fine-tunes nascent; no native picture/imaginative and prescient.

Optimized for latency

•  GPT-OSS-120B can run on 1×A100/H100 (80GB), and OSS-20B on a 16GB GPU.
•  Robust chain-of-thought & device use help.
  

2. GLM-4.7

GLM-4.7  is a 355B-parameter open mannequin with task-oriented reasoning enhancements. It was designed not only for Q&A however for end-to-end agentic coding and problem-solving. GLM-4.7 introduces “think-before-acting” and multi-turn reasoning controls to stabilize advanced duties. For instance, it implements “Interleaved Reasoning”, which means it performs a chain-of-thought earlier than each device name or response. It additionally has “Retention-Primarily based” and “Spherical-Degree” reasoning modes to maintain or skip internal monologue as wanted. These options let it adaptively commerce latency for accuracy.

Efficiency‑smart,  GLM‑4.7 leads open-source fashions throughout reasoning, coding, and agent duties. On the Humanity’s Final Examination (HLE) benchmark with device use, it scores ~42.8 %, a big enchancment over GLM‑4.6 and aggressive with different high-performing open fashions. In coding, GLM‑4.7 achieves ~84.9 % on LiveCodeBench v6 and ~73.8 % on SWE-Bench Verified, surpassing earlier GLM releases.

The mannequin additionally demonstrates sturdy agent functionality on benchmarks comparable to BrowseComp and τ²‑Bench, showcasing multi-step reasoning and gear integration. Collectively, these outcomes replicate GLM-4.7’s broad functionality throughout logic, coding, and agent workflows, in an open-weight mannequin launched below the MIT license.

Key Specs

• Structure: Sparse Combination-of-Specialists
• Whole parameters: ~355B (reported)
• Lively parameters: ~32B per token (reported)
• Context size: As much as ~200K tokens
• Major use circumstances: Coding, math reasoning, agent workflows
• Availability: Open-weight; business use permitted (license varies by launch)

Strengths

• Robust efficiency in multi-step reasoning and coding
• Designed for agent-style execution loops
• Lengthy-context help for advanced duties
• Aggressive with main open reasoning fashions

Weaknesses

• Excessive inference value resulting from scale
• Superior reasoning will increase latency
• Restricted English-first documentation
  

3. Kimi K2 Pondering

Kimi K2 Pondering is a trillion-parameter Combination-of-Specialists mannequin designed particularly for deep reasoning and gear use. It options roughly 1 trillion whole parameters however prompts solely 32 billion per token throughout 384 consultants. The mannequin helps a local context window of 256K tokens, which extends to 1 million tokens utilizing Yarn. Kimi K2 was skilled in INT4 precision, delivering as much as 2x sooner inference speeds.

The structure is absolutely agentic and at all times thinks first. In keeping with the mannequin card, Kimi K2-Pondering solely helps pondering mode, the place the system immediate routinely inserts a tag. Each output consists of inside reasoning content material by default.

Kimi K2 Pondering leads throughout the proven benchmarks, scoring 44.9% on Humanity’s Final Examination, 60.2% on BrowseComp, and 56.3% on Seal-0 for real-world data assortment. It additionally performs strongly in agentic coding and multilingual duties, reaching 61.1% on SWE-Multilingual, 71.3% on SWE-bench Verified, and 83.1% on LiveCodeBench V6.

General, these outcomes present Kimi K2 Pondering outperforming GPT-5 and Claude Sonnet 4.5 throughout reasoning, agentic, and coding evaluations.

Key Specs

• Structure: Massive-scale MoE
• Whole parameters: ~1T (reported)
• Lively parameters: ~32B per token
• Specialists: 384
• Context size: 256K (as much as ~1M with scaling)
• Major use circumstances: Deep reasoning, planning, long-context brokers
• Availability: Open-weight; business use permitted

Strengths

• Wonderful long-horizon reasoning
• Very giant context window
• Robust tool-use and planning functionality
• Environment friendly inference relative to whole measurement

Weaknesses: 

• Really monumental scale (1T) means daunting coaching/inference overhead. 
• Nonetheless early (new launch), so real-world adoption/tooling is nascent.

4. MiniMax-M2.1

MiniMax-M2.1 is one other agentic LLM geared towards tool-interactive reasoning. It makes use of a 230B whole param design with solely 10B activated per token, implying a big MoE or related sparsity. 

The mannequin helps interleaved reasoning and motion, permitting it to purpose, name instruments, and react to observations throughout prolonged agent loops. This makes it well-suited for duties involving lengthy sequences of actions, comparable to internet navigation, multi-file coding, or structured analysis duties.

MiniMax stories robust inside outcomes on agent benchmarks comparable to SWE-Bench, BrowseComp, and xBench. In observe, M2.1 is commonly paired with inference engines like vLLM to help perform calling and multi-turn agent execution.

Key Specs

• Structure: Sparse, agent-optimized LLM
• Whole parameters: ~230B (reported)
• Lively parameters: ~10B per token
• Context size: Lengthy context (precise measurement not publicly specified)
• Major use circumstances: Software-based brokers, lengthy workflows
• Availability: Open-weight (license particulars restricted)

Strengths

• Objective-built for agent workflows
• Excessive reasoning effectivity per lively parameter
• Robust long-horizon process dealing with

Weaknesses

• Restricted public benchmarks and documentation
• Smaller ecosystem than friends
• Requires optimized inference setup

5. DeepSeek-R1-Distill-Qwen3-8B

DeepSeek-R1-Distill-Qwen3-8B represents one of the spectacular achievements in environment friendly reasoning fashions. Launched in Might 2025 as a part of the DeepSeek-R1-0528 replace, this 8-billion parameter mannequin demonstrates that superior reasoning capabilities could be efficiently distilled from large fashions into compact, accessible codecs with out vital efficiency degradation.

The mannequin was created by distilling chain-of-thought reasoning patterns from the complete 671B parameter DeepSeek-R1-0528 mannequin and making use of them to fine-tune Alibaba’s Qwen3-8B base mannequin. This distillation course of used roughly 800,000 high-quality reasoning samples generated by the complete R1 mannequin, specializing in mathematical problem-solving, logical inference, and structured reasoning duties. The result’s a mannequin that achieves state-of-the-art efficiency amongst 8B-class fashions whereas requiring solely a single GPU to run.

Efficiency-wise, DeepSeek-R1-Distill-Qwen3-8B delivers outcomes that defy its compact measurement. It outperforms Google’s Gemini 2.5 Flash on AIME 2025 mathematical reasoning duties and practically matches Microsoft’s Phi 4 reasoning mannequin on HMMT benchmarks. Maybe most remarkably, this 8B mannequin matches the efficiency of Qwen3-235B-Pondering on sure reasoning duties—a 235B parameter mannequin. The R1-0528 replace considerably improved reasoning depth, with accuracy on AIME 2025 leaping from 70% to 87.5% in comparison with the unique R1 launch.

The mannequin runs effectively on a single GPU with 40-80GB VRAM (comparable to an NVIDIA H100 or A100), making it accessible to particular person researchers, small groups, and organizations with out large compute infrastructure. It helps the identical superior options as the complete R1-0528 mannequin, together with system prompts, JSON output, and performance calling—capabilities that make it sensible for manufacturing functions requiring structured reasoning and gear integration.

Key Specs

• Mannequin sort: Distilled reasoning mannequin
• Base structure: Qwen3-8B (dense transformer)
• Whole parameters: 8B
• Coaching strategy: Distillation from DeepSeek-R1-0528 (671B) utilizing 800K reasoning samples
• {Hardware} necessities: Single GPU with 40-80GB VRAM
• License: MIT License (absolutely permissive for business use)
• Major use circumstances: Mathematical reasoning, logical inference, coding help, resource-constrained deployments

Strengths

• Distinctive performance-to-size ratio: matches 235B fashions on particular reasoning duties at 8B measurement
• Runs effectively on single consumer-grade GPU, dramatically reducing deployment obstacles
• Outperforms a lot bigger fashions like Gemini 2.5 Flash on mathematical reasoning
• Absolutely open-source with permissive MIT licensing allows unrestricted business use
• Helps trendy options: system prompts, JSON output, perform calling for manufacturing integration
• Demonstrates profitable distillation of superior reasoning from large fashions to compact codecs

Weaknesses

• Whereas spectacular for its measurement, nonetheless trails the complete 671B R1 mannequin on probably the most advanced reasoning duties
• 8B parameter restrict constrains multilingual capabilities and broad area data
• Requires particular inference configurations (temperature 0.6 really helpful) for optimum efficiency
• Nonetheless comparatively new (Might 2025 launch) with restricted manufacturing battle-testing in comparison with extra established fashions

6. DeepSeek-V3.2 Terminus

DeepSeek’s V3 collection (codename “Terminus”) builds on the R1 fashions and is designed for agentic AI workloads. It makes use of a Combination-of-Specialists transformer with ~671B whole parameters and ~37B lively parameters per token.

DeepSeek-V3.2 introduces a Sparse Consideration structure for long-context scaling. It replaces full consideration with an indexer-selector mechanism, decreasing quadratic consideration value whereas sustaining accuracy near dense consideration.

As proven within the beneath determine, the eye layer combines Multi-Question Consideration, a Lightning Indexer, and a High-Okay Selector. The indexer identifies related tokens, and a focus is computed solely over the chosen subset, with RoPE utilized for positional encoding.

The mannequin is skilled with large-scale reinforcement studying on duties comparable to math, coding, logic, and gear use. These abilities are built-in right into a shared mannequin utilizing Group Relative Coverage Optimization.

                                  Fig- Consideration-architecture of deepseek-v3.2

DeepSeek stories that V3.2 achieves reasoning efficiency akin to main proprietary fashions on public benchmarks. The V3.2-Speciale variant is additional optimized for deep multi-step reasoning.

DeepSeek-V3.2 is MIT-licensed, out there by way of manufacturing APIs, and outperforms V3.1 on blended reasoning and agent duties.

Key specs

• Structure: MoE transformer with DeepSeek Sparse Consideration
• Whole parameters: ~671B (MoE capability)
• Lively parameters: ~37B per token
• Context size: Helps prolonged contexts as much as ~1M tokens with sparse consideration
• License: MIT (open-weight)
• Availability: Open weights + manufacturing API by way of DeepSeek.ai

Strengths

• State-of-the-art open reasoning: DeepSeek-V3.2 persistently ranks on the prime of open-source reasoning and agent duties.
• Environment friendly long-context inference: DeepSeek Sparse Consideration (DSA) reduces value progress on very lengthy sequences relative to straightforward dense consideration with out considerably hurting accuracy.
• Agent integration: Constructed-in help for pondering modes and mixed device/chain-of-thought workflows makes it well-suited for autonomous techniques.
  
• Open ecosystem: MIT license and API entry by way of internet/app ecosystem encourage adoption and experimentation. 

Weaknesses

• Massive compute footprint: Regardless of sparse inference financial savings, the general mannequin measurement and coaching value stay vital for self-hosting.
• Advanced tooling: Superior pondering modes and full agent workflows require experience to combine successfully.
• New launch: As a comparatively current era, broader group benchmarks and tooling help proceed to mature.
  

7. Qwen3-Subsequent-80B-A3B

Qwen3-Subsequent is Alibaba’s next-gen open mannequin collection emphasizing each scale and effectivity. The 80B-A3B-Pondering variant is specifically designed for advanced reasoning: it combines hybrid consideration (linearized + sparse mechanisms) with a high-sparsity MoE. Its specs are placing: 80B whole parameters, however solely ~3B lively (512 consultants with 10 lively). This yields very quick inference. Qwen3-Subsequent additionally makes use of multi-token prediction (MTP) throughout coaching for pace.

Benchmarks present Qwen3-Subsequent-80B performing excellently on multi-hop duties. The mannequin card highlights that it outperforms earlier Qwen-30B and Qwen-32B pondering fashions, and even outperforms the proprietary Gemini-2.5-Flash on a number of benchmarks. For instance, it will get ~87.8% on AIME25 (math) and ~73.9% on HMMT25, higher than Gemini-2.5-Flash’s 72.0% and 73.9% respectively. It additionally reveals robust efficiency on MMLU and coding checks.

Key specs: 80B whole, 3B lively. 48 layers, hybrid format with 262K native context. Absolutely Apache-2.0 licensed.

Strengths: Wonderful reasoning & coding efficiency per compute (beats bigger fashions on many duties); enormous context; extraordinarily environment friendly (10× pace up for >32K context vs older Qwens).

Weaknesses: As a MoE mannequin, it could require particular runtime help; “Pondering” mode provides complexity (at all times generates a block and requires particular prompting).

8. Qwen3-235B-A22B

Qwen3-235B-A22B represents Alibaba’s most superior open reasoning mannequin thus far. It makes use of a large Combination-of-Specialists structure with 235 billion whole parameters however prompts solely 22 billion per token, reaching an optimum steadiness between functionality and effectivity. The mannequin employs the identical hybrid consideration mechanism as Qwen3-Subsequent-80B (combining linearized and sparse consideration) however scales it to deal with much more advanced reasoning chains.

The “A22B” designation refers to its 22B lively parameters throughout a extremely sparse professional system. This design permits the mannequin to take care of reasoning high quality akin to a lot bigger dense fashions whereas conserving inference prices manageable. Qwen3-235B-A22B helps dual-mode operation: it could actually run in customary mode for fast responses or change to “pondering mode” with specific chain-of-thought reasoning for advanced duties.

Efficiency-wise, Qwen3-235B-A22B excels throughout mathematical reasoning, coding, and multi-step logical duties. On AIME 2025, it achieves roughly 89.2%, outperforming many proprietary fashions. It scores 76.8% on HMMT25 and maintains robust efficiency on MMLU-Professional (78.4%) and coding benchmarks like HumanEval (91.5%). The mannequin’s long-context functionality extends to 262K tokens natively, with optimized dealing with for prolonged reasoning chains.

The structure incorporates multi-token prediction throughout coaching, which improves each coaching effectivity and the mannequin’s capacity to anticipate reasoning paths. This makes it notably efficient for duties requiring ahead planning, comparable to advanced mathematical proofs or multi-file code refactoring.

Key Specs

• Structure: Hybrid MoE with dual-mode (customary/pondering) operation
• Whole parameters: ~235B
• Lively parameters: ~22B per token
• Context size: 262K tokens native
• License: Apache-2.0
• Major use circumstances: Superior mathematical reasoning, advanced coding duties, multi-step drawback fixing, long-context evaluation

Strengths

• Distinctive mathematical and logical reasoning efficiency, surpassing many bigger fashions
• Twin-mode operation permits flexibility between pace and reasoning depth
• Extremely environment friendly inference relative to reasoning functionality (22B lively vs. 235B whole)
• Native long-context help with out requiring extensions or particular configurations
• Complete Apache-2.0 licensing allows business deployment

Weaknesses

• Requires MoE-aware inference runtime (vLLM, DeepSpeed, or related)
• Pondering mode provides latency and token overhead for easy queries
• Much less mature ecosystem in comparison with LLaMA or GPT variants
• Documentation primarily in Chinese language, with English supplies nonetheless creating

9. MiMo-V2-Flash

MiMo-V2-Flash represents an aggressive push towards ultra-efficient reasoning by means of a 309 billion parameter Combination-of-Specialists structure that prompts solely 15 billion parameters per token. This 20:1 sparsity ratio is among the many highest in manufacturing reasoning fashions, enabling inference speeds of roughly 150 tokens per second whereas sustaining aggressive efficiency on mathematical and coding benchmarks.

The mannequin makes use of a sparse gating mechanism that dynamically routes tokens to specialised professional networks. This structure permits MiMo-V2-Flash to realize outstanding value effectivity, working at simply 2.5% of Claude’s inference value whereas delivering comparable efficiency on particular reasoning duties. The mannequin was skilled with a give attention to mathematical reasoning, coding, and structured problem-solving.

MiMo-V2-Flash delivers spectacular benchmark outcomes, reaching 94.1% on AIME 2025, putting it among the many prime performers for mathematical reasoning. In coding duties, it scores 73.4% on SWE-Bench Verified and demonstrates robust efficiency on customary programming benchmarks. The mannequin helps a 128K token context window and is launched below an open license allowing business use.

Nonetheless, real-world efficiency reveals some limitations. Neighborhood testing signifies that whereas MiMo-V2-Flash excels on mathematical and coding benchmarks, it could actually battle with instruction following and general-purpose duties exterior its core coaching distribution. The mannequin performs finest when duties intently match mathematical competitions or coding challenges however reveals inconsistent high quality on open-ended reasoning duties.

Key Specs

• Structure: Extremely-sparse MoE (309B whole, 15B lively)
• Whole parameters: ~309B
• Lively parameters: ~15B per token (20:1 sparsity)
• Context size: 128K tokens
• License: Open-weight, business use permitted
• Inference pace: ~150 tokens/second
• Major use circumstances: Mathematical competitions, coding challenges, cost-sensitive deployments

Strengths

• Distinctive effectivity with 15B lively parameters delivering robust math and coding efficiency
• Excellent value profile at 2.5% of Claude’s inference value
• Quick inference at 150 t/s allows real-time functions
• Robust mathematical reasoning with 94.1% AIME 2025 rating
• Latest launch represents cutting-edge MoE effectivity strategies

Weaknesses

• Instruction-following could be inconsistent on general-purpose duties
• Efficiency is strongest inside math and coding domains, much less dependable on numerous workloads
• Restricted ecosystem maturity with sparse group tooling and documentation
• Greatest suited to slender, well-defined use circumstances fairly than common reasoning brokers

10. Ministral 14B Reasoning

Mistral AI’s Ministral 14B Reasoning represents a breakthrough in compact reasoning fashions. With solely 14 billion parameters, it achieves reasoning efficiency that rivals fashions 5-10× its measurement, making it probably the most environment friendly mannequin on this top-10 listing. Ministral 14B is a part of the broader Mistral 3 household and inherits architectural improvements from Mistral Massive 3 whereas optimizing for deployment in resource-constrained environments.

The mannequin employs a dense transformer structure with specialised reasoning coaching. Not like bigger MoE fashions, Ministral achieves its effectivity by means of cautious dataset curation and reinforcement studying targeted particularly on mathematical and logical reasoning duties. This focused strategy permits it to punch nicely above its weight class on reasoning benchmarks.

Remarkably, Ministral 14B achieves roughly 85% accuracy on AIME 2025, a number one end result for any mannequin below 30B parameters and aggressive with fashions a number of occasions bigger. It additionally scores 68.2% on GPQA Diamond and 82.7% on MATH-500, demonstrating broad reasoning functionality throughout totally different drawback varieties. On coding benchmarks, it achieves 78.5% on HumanEval, making it appropriate for AI-assisted improvement workflows.

The mannequin’s small measurement allows deployment eventualities unimaginable for bigger fashions. It might run successfully on a single shopper GPU (RTX 4090, A6000) with 24GB VRAM, and even on high-end laptops with quantization. Inference speeds attain 40-60 tokens per second on shopper {hardware}, making it sensible for real-time interactive functions. This accessibility opens reasoning-first AI to a wider vary of builders and use circumstances.

Key Specs

• Structure: Dense transformer with reasoning-optimized coaching
• Whole parameters: ~14B
• Lively parameters: ~14B (dense)
• Context size: 128K tokens
• License: Apache-2.0
• Major use circumstances: Edge reasoning, native improvement, resource-constrained environments, real-time interactive AI

Strengths

• Distinctive reasoning efficiency relative to mannequin measurement (~85% AIME 2025 at 14B)
• Runs on shopper {hardware} (single RTX 4090 or related) with robust efficiency
• Quick inference speeds (40-60 t/s) allow real-time interactive functions
• Decrease operational prices make reasoning AI accessible to smaller groups and particular person builders
• Apache-2.0 license with minimal deployment obstacles

Weaknesses

• Decrease absolute ceiling than 100B+ fashions on probably the most tough reasoning duties
• Restricted context window (128K) in comparison with million-token fashions
• Dense structure means no parameter effectivity positive aspects from sparsity
• Might battle with extraordinarily lengthy reasoning chains that require sustained computation
• Smaller mannequin capability limits multilingual and multimodal capabilities

Mannequin Comparability Abstract

Conclusion

Open-source reasoning fashions have superior shortly, however operating them effectively stays an actual problem. Agentic and reasoning workloads are essentially token-intensive. They contain lengthy contexts, multi-step planning, repeated device calls, and iterative execution. Because of this, they burn by means of tokens quickly and change into costly and gradual when run on customary inference setups.

The Clarifai Reasoning Engine is constructed particularly to handle this drawback. It’s optimized for agentic and reasoning workloads, utilizing optimized kernels and adaptive strategies that enhance throughput and latency over time with out compromising accuracy. Mixed with Compute Orchestration, Clarifai dynamically manages how these workloads run throughout GPUs, enabling excessive throughput, low latency, and predictable prices whilst reasoning depth will increase.

These optimizations are mirrored in actual benchmarks. In evaluations revealed by Synthetic Evaluation on GPT-OSS-120B, Clarifai achieved industry-leading outcomes, exceeding 500 tokens per second with a time to first token of round 0.3 seconds. The outcomes spotlight how execution and orchestration selections straight impression the viability of huge reasoning fashions in manufacturing.

In parallel, the platform continues so as to add and replace help for prime open-source reasoning fashions within the group. You can attempt these fashions straight within the Playground or entry them by means of the API and combine them into their very own functions. The identical infrastructure additionally helps deploying customized or self-hosted fashions, making it simple to judge, evaluate, and run reasoning workloads below constant circumstances.

As reasoning fashions proceed to evolve in 2026, the flexibility to run them effectively and affordably would be the actual differentiator.